Typically, a broadcast channel refers to transmitting information from a base station having a plurality of antennas to all multiple users within one cell.
Here, each user owns a terminal having a single antenna. In this environment, the users cannot cooperate with each other, and thus it is difficult to eliminate interference between the users.
For this reason, many studies have recently been made of precoding techniques capable of eliminating interference between users in advance on the assumption that a base station can use channel information of all users.
Among these techniques, Sphere Encoding (SE) algorithm based on Vector Perturbation (VP) technique capable of driving optimal performance has been proposed.
Here, the SE algorithm exerts maximum performance, but it has high complexity and distribution, which serve as great obstacle factors on designing a system.
Generally, since the broadcast channel transmits signals to all the users who cannot cooperate with each other through a plurality of transmission antennas, there is a demand for a technique capable of efficiently eliminate the interference between the users in advance at a transmitting end.
First, a signal “y” transmitted from a base station 1 to multiple users at the same time is transmitted to receiving ends, i.e., user terminals 2, after previous elimination of the interference between the users and power normalization.
This signal can be expressed by Equation 1:
                                                                        y                =                                  Hx                  +                  n                                                                                                        =                                                      H                    ⁢                                          Ps                                              γ                                                                              +                  n                                                                    ⁢                                  ⁢                  (                      γ            =                                                          Ps                                            2                                )                                    [                  Eqn          .                                          ⁢          1                ]            
where H is the Rayleigh flat-fading channel matrix, n is the Gaussian noise vector, P is the precoding matrix for eliminating the interference between the users, s is the symbol vector of data to be transmitted, and γ is the normalized transmission power.
Among the techniques proposed to overcome this interference, the simplest technique is linear technique that includes channel inversion technique based on Zero-Forcing (ZF) technique, regularized channel inversion technique based on Minimum-Mean Square Error (MMSE) technique, and so forth.
The linear technique distorts a signal simply using an inverse matrix of the channel matrix “H” as the preceding matrix, and then transmits the distorted signal.
However, as well known through a linear detection technique of Multi-Input Multi-Output (MIMO) receiver, when an eigenvalue of the channel matrix is small, an eigenvalue of its inverse matrix increases.
This phenomenon increases the normalized transmission power “γ.”
Accordingly, Signal-to-Noise Ratio (SNR) of the receiving end is lowered to degrade performance.
In order to prevent this power loss, Tomlinson-Harashima Precoding (THP) is proposed which restores to original information by expanding an existing constellation to infinity to select a point corresponding to low power loss and by using a modulo technique at the receiving end, i.e., the user terminal.
This THP technique considerably improves performance compared to the existing linear technique, but it still does not obtain the optimal performance.
Afterwards, Vector Perturbation (VP) is proposed which derives optimal performance by adding a distortion value that expands the constellation to infinity on the basis of the THP technique and minimizes the transmission power.
This technique can be divided into a ZF-VP based on the ZF, and MMSE-VP based on the MMSE rather than minimum transmission power, wherein it is known that the latter shows better performance than the former.
Further, Lattice Reduction (LR) technique is introduced that can improve performance through channel orthogonalization on the assumption that a channel environment gradually varies.
Among these techniques, the linear techniques have low complexity and difficulty in obtaining the maximum performance, the non-linear techniques have maximum diversity gain of the system, and improvement in performance. In the case of the SE algorithm, the complexity is increased due to search for a maximum approximation lattice point in an infinite lattice space, and shows a characteristic that it is irregular depending on a channel environment. In other words, the SE algorithm has a characteristic that a search frequency varies depending upon a channel state, and encounters the following problems due to a long delay time when the channel state is bad.
In the event of downlink, the channel of which the transmitting end, i.e., the base station, is aware obtains information through feedback of the terminal. In this case, as the delay time increases, an error in channel information increases due to time variation of the channel.
Further, irregularity of the delay time makes it difficult to correct the error in channel information or to use, for instance, a buffer.